SynthesisFrontiers in medicine2025
Application of artificial intelligence in echocardiography from 2009 to 2024: a bibliometric analysis.
Synthesis in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review.Journal of clinical medicine · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Echocardiography is a cornerstone in the clinical diagnosis of cardiovascular diseases, providing critical insights into cardiac structure and function. Over recent years, artificial intelligence (AI) has emerged as a transformative adjunct to traditional echocardiographic techniques, enhancing diagnostic accuracy through innovations such as automatic view labeling, advanced image segmentation, and predictive disease modeling. The objective of this study is to explore the current status and prevailing research trends in this field from 2009 to 2024 through bibliometric analysis and to forecast future developmental trajectories. Methods: We selected the Science Citation Index Expanded (SCI-Expanded) from the Web of Science Core Collection (WOSCC) as our primary data source and conducted a comprehensive search encompassing all articles and reviews published between 2009 and 2024 and used the online analysis platform of bibliometrics, CiteSpace and VOSviewer software to analyze countries/regions, institutions, authors, keywords, and references, used Microsoft Excel 2021 to visualize the trends of the number of articles published by year. Results: Between 2009 and 2024, a total of 3,411 publications on AI applications in echocardiography were identified, including 3,000 articles (87.9%) and 411 reviews (12.1%), contributed by researchers from 100 countries/regions. China and the USA were the leading contributors in terms of publication volume. Notably, institutions such as Shanghai Jiaotong University demonstrated strong research productivity and international collaboration. Conclusion: Over the past decade, the integration of AI with echocardiography has become increasingly sophisticated. This study highlights the critical contributions of AI applications in echocardiography to the progression of the field and offers valuable insights for researchers embarking on future investigations.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.